1. Field of the Invention
The present invention relates to the field of imaging technology, correction or adjustment of images and image data, the adjustment of image properties, and the use of look-up-tables and sliders on view screens to systematically adjust the visual properties of images and image data.
2. Background of the Art
Images and image data can be represented in many different terms and in many different scientific formats, and many different types of properties can be used to assist in defining the image and the components of the image. A basic point or component in defining an image is known in the art as a pixel, the pixel being the smallest unit of information used to construct or define an image or the properties of an image. A pixel, depending upon the method in which the image is rendered, may vary in absolute size or proportions to the image, and the pixel itself may be composed of smaller units that need not be defined in the image data formatting system that is used to store the image data. For a particular example of this, an image rendered on a laser imaging system may have the data expressed in terms of pixels, but the physical process of writing or printing an image requires that the laser imager address a number of spots to create the pixel. The shape of the pixels may also be defined and preselected, for example, by designing an ordering of spots (number of spots, arrangement of spots, spacing of spots, vacant area between spots, distribution or gradation of spots as a function of density and color, and any other elements that relate to how spots are used to construct a pixel. The pixel, however, remains the basic building block of the image, and remains the basic building block of image data.
Each pixel may be defined by a range of properties, and groups of pixels may also be defined by their individual properties and their properties in relationship to the properties of other pixels. In the different options that are available for the definition of the properties of pixels, many of the terms used in defining the properties are related to each other, overlap each other or are the same name, although used in the different definition systems. For example, properties defined by such as terms as brightness, lightness, darkness, density, optical density, maximum density, minimum density, hue, chroma, saturation, contrast, luminosity, tone, gamma, contrast, tristimulus values, C*I*e*, L*a*b*, and spectra are examples of the many specific or general terms that are used within the imaging art. These terms are used, sometimes in association with general or specific mathematical terms or systems to define an image, define the pixels, and define the components of the image.
The advent of digital imaging, and more particularly the ability to store, access and print images has greatly enhanced the ability of hardware, software and apparatus to adjust the content of images and image data by addressing the properties of individual pixels and collections of pixels.
With the advent of digital reproduction machines, the copy process for making a permanent record of an image has changed. In a digital process, a document or image is scanned by a digital scanner to convert the light reflected from the document into storable data representing the light intensity from predetermined areas (pixels) of the document. These data, after suitable processing, are converted into image signals or pixels of image data to be used by the digital reproduction machine to recreate the scanned image. The pixels of image data are processed by an image processing system which converts the pixels of image data into signals which can be utilized by a printing device to recreate the scanned image. This printing device may be either a xerographic printer, ink jet printer, thermal printer, laser printer, video monitor, or any other type of printing device that is capable of converting digital data into a mark on a recording medium.
As with the light-lens systems, the quality of a reproduction machine is still a function of how well the copy matches the original. However, in this digital environment, other factors can now contribute to or impact the quality of the reproduced image. For example, the scanner can impact the quality if the scanner is not properly calibrated. Also, the output (printing) device can impact the quality if a printhead is clogged or a photoreceptor is not properly cleaned. But, the aspect of the digital system which can have the greatest impact is the digital (image) processing of the image data because a digital machine must convert light to a digital signal and then convert the digital signal to a mark on a recording medium. In other words, the image processing system provides the transfer function between the light reflected from the document to the mark on the recording medium.
Quality can be measured in many different ways. One way is to look at the characteristics of the reproduced image. An example of such a characteristic for determining the quality of the reproduced image is the contrast of the image. The contrast of an imaged (copied) document is the most commonly used characteristic for measuring quality since contrast provides a good overall assessment of the image's quality.
In a digital reproduction machine, the image processing system can greatly impact the contrast of the image. Thus, to assure high quality at the output printing device, it is desirable to know the contrast of the image being scanned prior to the image processing stage because, with this knowledge, the image processing system can process the image data so that the reproduced image has the proper contrast. One way of obtaining this contrast information prior to digital image processing is for the digital reproduction machine to generate a gray level histogram, which gives an easy to read measure of the image contrast. The image or gray level histogram describes the statistical distribution of gray levels of an image in terms of the number of pixels at each gray level.
A histogram can be represented graphically with a range of intensity on the horizontal axis (e.g., from 0 to 255, if an eight-bit per pixel sampling resolution is utilized), and the number of pixels on the vertical axis. Using this graphical representation, a histogram can illustrate whether an image is basically dark or light and high or low contrast, and what the distribution of data are within the image. It is important to know that when an image is represented by histogram, all spatial information is lost. The histogram specifies the number of pixels of each gray level but gives no indication where these pixels are located in the image. In other words, very different images may have very similar histograms.
Conventionally, when creating a histogram of the scanned image, a digital reproduction system samples a document, collects intensity data from the document, and uses this information to determine the document's background value. In such conventional systems, the computed background value of the document represents the average intensity of the document.
Histograms of low contrast images appear as narrow distributions that do not span the full tone range. Excessively dark images have histograms with large peaks in regions of low intensity and excessively bright images have large peaks in regions of high intensity. Histograms of images with proper contrast have a distribution spanning the full tone range, which is proportional to the amounts of objects of various brightness in the original scene from which the image was derived.
Enhancement of an image (to correct for image degradations such as under or over-exposure, poor lighting, printer preferences, etc.) can be achieved by modifying the histogram of an image. This “contrast enhancement”, is often made up of a combination of two linear transformations known as histogram slide and histogram stretch. These operations, based on an image's contrast and dynamic range characteristics, redistribute the histogram so that contrast and dynamic range may be enhanced. The objective of contrast enhancement is to utilize the full dynamic range to reveal the intensity variations (details) within the image that may not be visible until after the transformation.
The histogram sliding operation is simply the addition or subtraction of a constant intensity level to all pixels in the image. Doing this to every pixel effectively slides the entire input image histogram to the right or left. The basic effect of histogram sliding is a lightening or darkening of the image. Since the resulting histogram is only shifted, the contrast of the output image will be identical to that of the input image. The linear transformation or tone reproduction curve (TRC) map for a sliding operation has been a 45 degree line (this is why the image contrast is maintained). For a slide of zero, the line would pass through the origin. For a positive slide (>0), the line will touch the vertical axis (output intensity) passing through the origin. For a negative slide, the line will touch the horizontal axis (input intensity) passing through the origin. A positive slide effectively lightens an image, while a negative slide darkens an image.
Histogram stretching is the multiplication of all pixels in the image by a constant value. For example, a histogram, with all the pixels residing in the lower half of the gray scale range, will spread out to occupy the entire gray scale range when multiplied by a constant of two (2). This stretching operation expands or reduces the contrast and dynamic range of an image. The TRC map will always be a straight line passing through the origin. For the case of a stretch of one (1), the line would be at a 45 degree angle. In general, contrast enhancement is carried out in conjunction with histogram sliding.
Typically, in a scanner, the histogram of an image is determined from a prescan. The minimum and maximum reflectance (or intensity) of the image area scanned, (Rmin and Rmax) respectively, are determined from this scan. The gray scale transformation of shifting and stretching the gray scale to occupy the entire dynamic range is simply a mapping function from the input gray scale into a transformed output gray scale. This is normally accomplished with a look-up table. The “classic” method of dynamic range modification effectively shifts the input gray scale by Rmin and then stretches the input dynamic range (Rmin to Rmax) to the available output dynamic range. Rmax is the image reflectance value such that the sum of the image area which contains reflectances above Rmax is less than a prescribed percentage of the total image area, and Rmin is the image reflectance value such that the sum of the image area which contains reflectances below Rmin is less than a prescribed percentage of the total image area. For example, the percentage can be around three percent. Defining Rmin and Rmin, allows a greater “range” to stretch the rest of the grey levels. However, this definition of Rmax and Rmin instead of the absolute minimum and maximum reflectance values within an image will cause equations and histograms defining the image data to effectively compress the gray level ranges by saturating them. This is usually tolerable, though, because, since by definition, very few pixels have gray levels in these ranges, hence, little image information should be lost.
In “Techniques for Image Processing and Classification in Remote Sensing,” by Robert A. Schowengerdt, Academic Press, 1983, it was contended that if the image histogram is asymmetric, it is impossible to simultaneously control the average gray level of the output image and the amount of saturation at the ends of the histogram with a simple linear transformation. The article suggests a two (or more) segment piecewise linear transformation, to make better use of the available gray level range. One would need to manually determine a series of linear steps designed to expand the individual intensity ranges in which the data fall to fill the available dynamic range. Thus, one could designate a series of Rmin and Rmax values and use various contrast enhancing, stretching, sliding, mapping or transforming equations, within each region. An improvement in the automation of this process and defining the boundaries of these segments in the TRC map relates to the present invention.
U.S. Pat. No. 6,236,751 describes an image desired to be reproduced is scanned to determine its video pixel gray values. A histogram generator generates a histogram distribution representing a frequency of the gray values. The histogram distribution is analyzed to determine minimum and maximum input gray values that define input boundaries. A segment point is computed between the input boundaries based on the histogram data. The segment point defines a plurality of input segments between the input boundaries. A dynamic output range is selected. Each input segment is mapped to an output segment based on a linear transformation for the corresponding segment. In this manner, a tone reproduction curve map having a piecewise linear transformation is automatically generated from the image histogram data.
U.S. Pat. No. 6,204,940 describes a process and apparatus to improve the digital processing of scanned negative films by reducing the amount of time necessary to perform the process and by increasing the robustness and quality of the images produced. These benefits are achieved by a process of color inversion, white point and black point mapping, and midtone adjustment. White and black mapping increases the dynamic range of the image, as well as removes the color cast of the negative film. A backlit image post-processing algorithm can be employed which uses heuristics to identify back-lighted situations, which are then brightened using a nonlinear power mapping. A midtone adjustment can include the sub-steps of contrast reduction and color adjustment. Contrast reduction reverses the film exposure characteristics. Color adjustment removes the remaining color cast in the midtone region of the image, and obtains the correct brightness. Starting from images with poor contrast and color cast, the system automatically looks for the appropriate correction parameters to produce images with vivid color and good contrast. This is achieved without rescanning or retaking the picture. One implementation, using one-dimensional look-up-tables, is very efficient.
U.S. Pat. No. 5,579,402 describes creating a histogram of an image signal matrix, representative of a radiographic image obtained by scanning a stimulable phosphor sheet carrying a radiation image with stimulating rays, detecting the light emitted after stimulation and converting the detected light into electrical image signals, can be achieved by creating a histogram of an image signal matrix by means of only a fraction of the image pixels comprised in the image signal matrix, the fraction being determined on the basis of statistical pixel sampling.
The correction of image data on a PC or networked computer can be complex, even though the process has improved significantly over the years. A number of process have been provided in software associated with imaging programs, and many separate functions have been provided. These changes are usually enabled with a screen viewable function, sometimes associated with visual images of graphic representation of data corresponding to the image data (e.g., a histogram, a D v loge curve, etc.). The screen viewable function could be, for example, luminosity, gamma, hue, tone, output/input range, etc. For example, a histogram may be displayed with a program to edit luminosity. Other common terms for luminosity include lightness, value, luminance, tone or brightness. The output maximum and output minimum may be adjusted (e.g., range compression) or the clipping limits may be adjusted (e.g., histogram stretching) and the effect of those changes is displayed on the displayed histogram. The contrast (gamma) may also be indicated by means of a numeric value or as a tone reproduction curve, for instance as a tone reproduction curve superimposed on or adjacent to the histogram. However, certain image data correction functions have never been combined on a single screen, and correction of certain features, such as the ability to alter mid-tones in a displayed histogram have been very complex. For example, histogram curves have been corrected by using a PC displayed cursor to grab various portions of the tone reproduction curve, move those various ‘grabbed’ portions of the curve (also shifting other portions of the curve with it), and thus manually adjusting the entire curve. It takes a very sophisticated and well-versed graphics expert to appreciate the nature of the effects brought on by the changes, and to envision the effects on the final image. Additionally, it is difficult for the ordinary operator to visualize the effect of combinations of different data modification programs on a single set of image data.
A commercial product for the review of assay data prior to printing out the data was originally distributed by Molecular Dynamics, 928 East Arques Avenue, Sunnyvale, Calif. as “ImageQuant 5.0.” This software program accesses an image of a series of assay stripes and displays the composite side-by-side assays on a preview screen. A set of sliders is provided to adjust the slope/bend in conjunction with a slider for altering brightness. This type of adjustment is uniquely directed and of interest in the apparatus to the review of chemical assays, which are visual representations of data, as the data is read in terms of contrast and differences of the stripes. The capability of the system is not directed to improving the quality or characteristics of the image, but directed towards an ability to read and emphasize visual representations of data.